Gastos Casa
14 agosto, 2023
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(format = "markdown", size=12)| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 30/6/2023 | Electricidad | 49877 | Andrés | enel |
| 30/6/2023 | Agua | 12706 | Andrés | NA |
| 1/7/2023 | Comida | 48400 | Andrés | de la ostia |
| 2/7/2023 | Comida | 70135 | Tami | Supermercado |
| 2/7/2023 | Parafina | 19418 | Tami | NA |
| 4/7/2023 | Comida | 12000 | Andrés | nueces y almendras 500 gr |
| 6/7/2023 | Gas | 68950 | Andrés | lipigas |
| 8/7/2023 | Agua | 12706 | Andrés | NA |
| 8/7/2023 | Comida | 57693 | Tami | Supermercado |
| 9/7/2023 | correo | 8000 | Andrés | correos de chile raul miranda |
| 9/7/2023 | mouse | 51980 | Andrés | NA |
| 9/7/2023 | lamina | 13800 | Andrés | NA |
| 12/7/2023 | Comida | 26780 | Andrés | NA |
| 12/7/2023 | Netflix | 11880 | Tami | Netflix junio y julio 2023 |
| 17/7/2023 | Comida | 86974 | Tami | Supermercado |
| 19/7/2023 | VTR | 21990 | Andrés | NA |
| 25/7/2023 | Comida | 75171 | Tami | Supermercado |
| 24/7/2023 | Enceres | 27065 | Andrés | secador platos |
| 30/7/2023 | Comida | 19630 | Andrés | choritos, costa rama y weas |
| 30/7/2023 | Parafina | 19920 | Tami | NA |
| 30/7/2023 | Electricidad | 49345 | Andrés | PAC ENEL 01686518 |
| 31/7/2023 | Comida | 78380 | Tami | Supermercado |
| 3/8/2023 | Comida | 19000 | Andrés | NA |
| 3/8/2023 | Diosi | 15980 | Andrés | NA |
| 6/8/2023 | Gas | 16650 | Andrés | NA |
| 6/8/2023 | Gas | 23666 | Tami | Parafina |
| 8/8/2023 | Comida | 78577 | Tami | Supermercado |
| 9/8/2023 | Agua | 11520 | Andrés | NA |
| 31/3/2019 | Comida | 9000 | Andrés | NA |
| 8/9/2019 | Comida | 24588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 6.9346e+08 2 7.2402 8e-04 ***
## lag_depvar 8.4623e+10 1 1767.0458 <2e-16 ***
## Residuals 2.8973e+10 605
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 1116.079 13341.60 0.0155225
## 2-0 28550.140 22979.938 34120.34 0.0000000
## 2-1 21321.302 18018.714 24623.89 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
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## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
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## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
## 486 27410.43 2 34690.43
## 487 41755.00 2 27410.43
## 488 49379.57 2 41755.00
## 489 57198.86 2 49379.57
## 490 51144.57 2 57198.86
## 491 56677.43 2 51144.57
## 492 65416.43 2 56677.43
## 493 69779.71 2 65416.43
## 494 54046.00 2 69779.71
## 495 43259.57 2 54046.00
## 496 40998.57 2 43259.57
## 497 41368.57 2 40998.57
## 498 42274.29 2 41368.57
## 499 35962.71 2 42274.29
## 500 38709.00 2 35962.71
## 501 44778.14 2 38709.00
## 502 51282.43 2 44778.14
## 503 52094.86 2 51282.43
## 504 52221.43 2 52094.86
## 505 45011.43 2 52221.43
## 506 46545.43 2 45011.43
## 507 42263.00 2 46545.43
## 508 45417.43 2 42263.00
## 509 45034.71 2 45417.43
## 510 37840.57 2 45034.71
## 511 39135.43 2 37840.57
## 512 38191.14 2 39135.43
## 513 39456.86 2 38191.14
## 514 42479.14 2 39456.86
## 515 34282.57 2 42479.14
## 516 28878.43 2 34282.57
## 517 56227.14 2 28878.43
## 518 65569.43 2 56227.14
## 519 69751.29 2 65569.43
## 520 62171.71 2 69751.29
## 521 63705.14 2 62171.71
## 522 79257.86 2 63705.14
## 523 87244.71 2 79257.86
## 524 58568.00 2 87244.71
## 525 52695.29 2 58568.00
## 526 48911.00 2 52695.29
## 527 53924.00 2 48911.00
## 528 53358.86 2 53924.00
## 529 42121.14 2 53358.86
## 530 47835.71 2 42121.14
## 531 62329.29 2 47835.71
## 532 56056.86 2 62329.29
## 533 59946.43 2 56056.86
## 534 64511.57 2 59946.43
## 535 61137.43 2 64511.57
## 536 55448.71 2 61137.43
## 537 47964.43 2 55448.71
## 538 46425.71 2 47964.43
## 539 55512.00 2 46425.71
## 540 55226.29 2 55512.00
## 541 46709.14 2 55226.29
## 542 49254.71 2 46709.14
## 543 49056.29 2 49254.71
## 544 49850.57 2 49056.29
## 545 39145.71 2 49850.57
## 546 29799.43 2 39145.71
## 547 34769.86 2 29799.43
## 548 44061.57 2 34769.86
## 549 43829.14 2 44061.57
## 550 45782.00 2 43829.14
## 551 38924.57 2 45782.00
## 552 49242.43 2 38924.57
## 553 50565.00 2 49242.43
## 554 38864.43 2 50565.00
## 555 49786.71 2 38864.43
## 556 58787.86 2 49786.71
## 557 58060.86 2 58787.86
## 558 62179.43 2 58060.86
## 559 57333.86 2 62179.43
## 560 70797.00 2 57333.86
## 561 89901.71 2 70797.00
## 562 78558.14 2 89901.71
## 563 65466.00 2 78558.14
## 564 70525.00 2 65466.00
## 565 68377.86 2 70525.00
## 566 69736.29 2 68377.86
## 567 60085.86 2 69736.29
## 568 41757.00 2 60085.86
## 569 49780.29 2 41757.00
## 570 56540.29 2 49780.29
## 571 57894.29 2 56540.29
## 572 60270.29 2 57894.29
## 573 61011.00 2 60270.29
## 574 57721.43 2 61011.00
## 575 71741.00 2 57721.43
## 576 59576.00 2 71741.00
## 577 52390.29 2 59576.00
## 578 61092.29 2 52390.29
## 579 62814.00 2 61092.29
## 580 54908.29 2 62814.00
## 581 62082.00 2 54908.29
## 582 57017.71 2 62082.00
## 583 53634.43 2 57017.71
## 584 69169.00 2 53634.43
## 585 52488.14 2 69169.00
## 586 60895.57 2 52488.14
## 587 59856.57 2 60895.57
## 588 52670.00 2 59856.57
## 589 51874.57 2 52670.00
## 590 52190.57 2 51874.57
## 591 41562.43 2 52190.57
## 592 44764.14 2 41562.43
## 593 38612.71 2 44764.14
## 594 43473.14 2 38612.71
## 595 53505.00 2 43473.14
## 596 45870.86 2 53505.00
## 597 52578.00 2 45870.86
## 598 55300.00 2 52578.00
## 599 61789.71 2 55300.00
## 600 57391.71 2 61789.71
## 601 62902.29 2 57391.71
## 602 53250.43 2 62902.29
## 603 55402.57 2 53250.43
## 604 56291.29 2 55402.57
## 605 58933.57 2 56291.29
## 606 59590.71 2 58933.57
## 607 59065.00 2 59590.71
## 608 52399.57 2 59065.00
## 609 60483.43 2 52399.57
## 610 58262.71 2 60483.43
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 453 50784.40 15033.613
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43 27410.43 41755.00 49379.57 57198.86 51144.57 56677.43
## [491] 65416.43 69779.71 54046.00 43259.57 40998.57 41368.57 42274.29
## [498] 35962.71 38709.00 44778.14 51282.43 52094.86 52221.43 45011.43
## [505] 46545.43 42263.00 45417.43 45034.71 37840.57 39135.43 38191.14
## [512] 39456.86 42479.14 34282.57 28878.43 56227.14 65569.43 69751.29
## [519] 62171.71 63705.14 79257.86 87244.71 58568.00 52695.29 48911.00
## [526] 53924.00 53358.86 42121.14 47835.71 62329.29 56056.86 59946.43
## [533] 64511.57 61137.43 55448.71 47964.43 46425.71 55512.00 55226.29
## [540] 46709.14 49254.71 49056.29 49850.57 39145.71 29799.43 34769.86
## [547] 44061.57 43829.14 45782.00 38924.57 49242.43 50565.00 38864.43
## [554] 49786.71 58787.86 58060.86 62179.43 57333.86 70797.00 89901.71
## [561] 78558.14 65466.00 70525.00 68377.86 69736.29 60085.86 41757.00
## [568] 49780.29 56540.29 57894.29 60270.29 61011.00 57721.43 71741.00
## [575] 59576.00 52390.29 61092.29 62814.00 54908.29 62082.00 57017.71
## [582] 53634.43 69169.00 52488.14 60895.57 59856.57 52670.00 51874.57
## [589] 52190.57 41562.43 44764.14 38612.71 43473.14 53505.00 45870.86
## [596] 52578.00 55300.00 61789.71 57391.71 62902.29 53250.43 55402.57
## [603] 56291.29 58933.57 59590.71 59065.00 52399.57 60483.43 58262.71
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6 7
## 1960.63935 4015.41263 -513.26126 2459.58014 -2920.57021 536.57855
## 8 9 10 11 12 13
## -5629.62927 -1217.30036 -3998.69989 -481.54500 -4994.20958 -1702.06489
## 14 15 16 17 18 19
## -991.79402 293.22082 -3307.28882 -461.90316 -2201.99916 6524.94566
## 20 21 22 23 24 25
## -1525.87635 -1215.68726 1462.13681 -1178.04218 235.33943 1703.32224
## 26 27 28 29 30 31
## -7072.16035 906.70289 8171.80907 489.22589 57.79523 -2332.53706
## 32 33 34 35 36 37
## 1616.63990 4629.36821 1228.70275 2497.36543 -1745.56884 4701.35269
## 38 39 40 41 42 43
## 4358.85941 -2182.89990 -2923.05278 -1089.04741 -10733.86923 7186.92726
## 44 45 46 47 48 49
## 2542.47902 1380.44253 8131.42897 792.18726 6628.88093 6868.91300
## 50 51 52 53 54 55
## -5678.38362 -4676.69650 -5004.36948 -7931.29271 6047.07951 -4085.98647
## 56 57 58 59 60 61
## -4943.62859 3763.27204 846.68995 -58.59603 119.18539 -5014.68670
## 62 63 64 65 66 67
## 18060.33980 3767.79602 -3497.42675 6018.63542 7486.47622 14838.77832
## 68 69 70 71 72 73
## 2017.70278 -12911.27707 -1176.72196 4743.88805 -4764.69552 -4335.38473
## 74 75 76 77 78 79
## -10481.27161 2374.86453 -5454.35557 961.79232 -6943.93275 409.86941
## 80 81 82 83 84 85
## -2468.16478 -2816.44840 -4065.75726 -693.84301 2173.14308 3663.09004
## 86 87 88 89 90 91
## 428.54007 -521.54780 159.77597 4272.16916 -1144.90914 1155.29565
## 92 93 94 95 96 97
## -2048.45491 -1050.81323 161.73530 263.18947 -7490.92919 2310.31043
## 98 99 100 101 102 103
## -8648.86394 -3067.70188 -4179.64737 -1899.42041 -1420.35337 3029.93373
## 104 105 106 107 108 109
## -2441.18965 2484.20725 -1227.55006 898.81470 2534.82529 -3173.35705
## 110 111 112 113 114 115
## -4771.09740 -939.57221 1817.41130 11638.11395 -1172.04014 2718.07091
## 116 117 118 119 120 121
## 4333.69252 3608.33694 -971.55873 -4614.74106 -3682.56606 2318.88492
## 122 123 124 125 126 127
## -1709.34653 1343.78733 8875.32886 952.37769 231.57897 -2431.02333
## 128 129 130 131 132 133
## 2708.91519 7127.03885 1149.59617 -8368.75364 1777.72156 4178.68214
## 134 135 136 137 138 139
## -3083.79727 -1381.25141 -834.22534 -3870.88044 1152.22505 -509.89122
## 140 141 142 143 144 145
## -2930.70104 1674.21411 -1901.56067 -7865.75751 1928.76692 -3555.27814
## 146 147 148 149 150 151
## 2001.40464 -323.83329 962.86789 -401.07295 1312.48537 1166.06819
## 152 153 154 155 156 157
## 3351.06643 -4832.15402 -1197.37921 -3267.26155 5896.91810 9755.19767
## 158 159 160 161 162 163
## -3425.92573 -4791.51447 3560.53572 209.58726 2727.26990 -5840.20473
## 164 165 166 167 168 169
## -6729.52068 4120.53277 17418.35533 3833.23764 -169.24525 -2235.03184
## 170 171 172 173 174 175
## -929.13740 3747.18693 -41.89818 -7900.48613 2952.17027 4444.95739
## 176 177 178 179 180 181
## 786.18518 8910.97608 -9009.33463 -3338.57431 -10647.91741 -11245.91496
## 182 183 184 185 186 187
## 1134.83369 9232.05700 -1379.59544 5972.50872 6663.65629 13327.37634
## 188 189 190 191 192 193
## 8714.05638 -3726.77174 2728.48959 10630.82023 -1308.62471 -2159.22752
## 194 195 196 197 198 199
## -10045.66241 -6245.29797 1287.31482 -5163.69142 -9772.90815 5324.07740
## 200 201 202 203 204 205
## -3056.97398 -1718.89665 -813.39650 6490.96925 9949.69019 737.25065
## 206 207 208 209 210 211
## 3077.87449 3268.05214 5970.88814 13059.26352 -5362.93723 -11055.79921
## 212 213 214 215 216 217
## -5550.18869 -10526.83716 -5107.82854 1464.24293 -13038.25970 16258.40773
## 218 219 220 221 222 223
## 7857.62281 1652.26618 26820.73345 12893.13441 7776.25197 14485.73770
## 224 225 226 227 228 229
## -3378.25212 -1301.90419 4153.04741 731.51713 3083.70366 9335.27306
## 230 231 232 233 234 235
## 6213.68554 -1504.97437 -1483.38928 9721.24551 -11152.86229 -7068.06596
## 236 237 238 239 240 241
## -8409.15354 -10053.49773 3038.38466 1358.29744 -8267.17030 -9027.27025
## 242 243 244 245 246 247
## 8992.06306 -7759.51795 2433.63892 -10313.14679 -4146.17768 1317.57059
## 248 249 250 251 252 253
## 936.30804 -12353.27497 3510.69425 1991.54365 4179.80178 2155.65218
## 254 255 256 257 258 259
## -1112.69323 11180.68882 21025.99025 3515.49802 -3959.07968 4350.68260
## 260 261 262 263 264 265
## -1440.48807 3951.29196 -4624.16313 -10730.20928 -4674.03427 -506.72774
## 266 267 268 269 270 271
## -5170.47560 8756.93195 -4213.64735 4217.86455 -2037.84441 4480.22395
## 272 273 274 275 276 277
## 797.84256 7393.46817 -1263.86712 12147.92654 -4369.77521 1876.97128
## 278 279 280 281 282 283
## -220.53635 7985.67293 -4868.93812 -2603.94432 -11166.48194 -2672.54428
## 284 285 286 287 288 289
## 18638.67625 7932.26506 2933.59031 -427.36611 1081.04581 6563.33218
## 290 291 292 293 294 295
## 7083.87527 -18535.74069 -11078.18079 -8148.16568 9588.03871 3094.67706
## 296 297 298 299 300 301
## -1124.68086 27449.46818 10333.32370 5219.60790 9839.51873 3217.20845
## 302 303 304 305 306 307
## -690.78460 8191.19449 -23970.33395 -3433.69558 -103.76766 -6895.74305
## 308 309 310 311 312 313
## -3950.72545 2933.05766 -9155.16404 -3254.63397 -8216.78239 1492.33060
## 314 315 316 317 318 319
## -3186.46911 2008.91126 -4086.42262 27424.73496 -540.67504 3452.26696
## 320 321 322 323 324 325
## 11002.79244 5828.15148 32637.58431 5587.49057 -20476.55444 2043.27091
## 326 327 328 329 330 331
## 1351.37298 -6237.70376 -1574.89215 -33130.86561 801.58881 -2341.05802
## 332 333 334 335 336 337
## -119.89216 -3168.11922 4085.29431 -385.10982 -6888.81769 -3089.50234
## 338 339 340 341 342 343
## -2167.81798 -7651.62128 3843.69149 -1327.93535 -1687.97241 -940.92335
## 344 345 346 347 348 349
## 237.61628 557.62289 -1528.02766 -9359.07059 -13181.02230 2262.65773
## 350 351 352 353 354 355
## -4325.36744 -3668.55337 -5991.81771 1720.90517 1392.68989 2790.60556
## 356 357 358 359 360 361
## -3695.63438 -462.41280 739.56094 7091.01578 409.73437 96.23386
## 362 363 364 365 366 367
## 2716.56420 -2599.59014 -747.34295 -8617.60201 -4555.01037 -6159.92810
## 368 369 370 371 372 373
## -4923.00362 -7239.67623 5000.79153 413.33219 7179.24918 -7518.08250
## 374 375 376 377 378 379
## -2190.76628 -3318.61634 -2407.32299 -12399.66967 1893.07740 -10607.16742
## 380 381 382 383 384 385
## 5671.71466 9374.19553 3239.56072 -2262.13363 1724.96800 6876.37623
## 386 387 388 389 390 391
## 11585.13746 -5564.49255 -5185.12925 -26.46093 8689.62684 1992.62521
## 392 393 394 395 396 397
## 11398.73608 -9643.36429 2921.52466 868.77948 712.84090 -508.99751
## 398 399 400 401 402 403
## -430.71531 -14364.56647 8555.73235 -1077.17669 -1272.32223 7077.76724
## 404 405 406 407 408 409
## -7788.33524 -1203.64896 -2438.15643 -5732.30997 -2796.95826 -3857.30542
## 410 411 412 413 414 415
## -8703.90398 6147.80390 1713.93372 -7278.35432 -7633.69990 14247.29657
## 416 417 418 419 420 421
## 3945.02299 4641.79252 -7864.25271 -4627.44688 -2507.98225 2906.80607
## 422 423 424 425 426 427
## -13896.73970 -2756.59855 -9061.29002 3014.24955 7020.79989 6677.20540
## 428 429 430 431 432 433
## -3839.99283 -3997.66762 -4619.44434 -1707.33273 -5628.92607 -6567.20731
## 434 435 436 437 438 439
## -5915.60610 -1376.57713 -817.73823 -4930.53264 2613.04629 4905.79822
## 440 441 442 443 444 445
## -4947.55690 -2073.13382 1659.26646 -3733.23228 2923.98731 -6461.79387
## 446 447 448 449 450 451
## -12028.47198 -4492.78932 9657.91521 -1935.45157 4848.84570 -5734.30748
## 452 453 454 455 456 457
## -1019.38285 490.04602 3144.38979 -12123.73219 3439.21329 -6594.80689
## 458 459 460 461 462 463
## 6595.32028 3141.35073 2661.01516 -3673.98906 2239.29849 155.62911
## 464 465 466 467 468 469
## 1956.36854 -346.11046 3520.62207 -2448.51076 5975.65115 -6734.57116
## 470 471 472 473 474 475
## -2805.50832 -2061.94757 -4528.46768 3109.11589 7939.34318 -5819.83682
## 476 477 478 479 480 481
## 1640.22262 -6009.52103 -2712.83836 2133.02568 -12786.54469 -9690.83576
## 482 483 484 485 486 487
## -1184.98517 53.90802 -906.64441 -1274.10110 -9509.07122 11123.00377
## 488 489 490 491 492 493
## 6358.62685 7592.81367 -5214.73946 5547.01095 9507.45889 6323.14933
## 494 495 496 497 498 499
## -13178.99552 -10376.71741 -3321.82345 -999.06994 -412.91284 -7506.72078
## 500 501 502 503 504 505
## 690.66766 4387.93123 5650.49204 845.37671 270.27952 -7049.03618
## 506 507 508 509 510 511
## 712.01061 -4895.28478 1957.74062 -1149.35291 -8012.95766 -504.74903
## 512 513 514 515 516 517
## -2567.36151 -486.09781 1443.03089 -9363.79221 -7688.81803 24327.28190
## 518 519 520 521 522 523
## 10049.35628 6162.57954 -5028.72838 3050.93403 17279.27502 11833.74940
## 524 525 526 527 528 529
## -23740.95839 -4846.51015 -3558.72004 4722.64671 -172.06419 -10921.68264
## 530 531 532 533 534 535
## 4498.54382 14056.62236 -4733.44110 4573.42662 5779.27082 -1537.64016
## 536 537 538 539 540 541
## -4312.21500 -6883.33981 -1958.11549 8457.10863 323.85948 -7946.52107
## 542 543 544 545 546 547
## 1955.03470 -441.92263 523.73950 -10867.11685 -10967.95924 2074.55789
## 548 549 550 551 552 553
## 7073.47172 -1183.91381 969.68446 -7574.36447 8666.03478 1077.40243
## 554 555 556 557 558 559
## -11765.43171 9262.26396 8830.17738 329.17769 5075.63580 -3327.01427
## 560 561 562 563 564 565
## 14321.09392 21798.12158 -6045.58160 -9340.64399 7025.62178 509.18215
## 566 567 568 569 570 571
## 3722.02945 -7101.63050 -17095.72061 6757.61380 6588.15810 2103.76194
## 572 573 574 575 576 577
## 3310.35537 1998.99432 -1930.30839 14930.36085 -9342.89537 -6022.08756
## 578 579 580 581 582 583
## 8885.98444 3092.05916 -6300.64478 7700.98252 -3559.01118 -2568.43502
## 584 585 586 587 588 589
## 15888.17226 -14209.39823 8604.75406 304.52644 -5984.69386 -573.31015
## 590 591 592 593 594 595
## 429.67611 -10471.38585 1909.51608 -7007.13090 3166.09013 9000.15030
## 596 597 598 599 600 601
## -7298.18727 6002.32102 2931.57589 7070.38568 -2932.57334 6376.41027
## 602 603 604 605 606 607
## -8034.75147 2453.39223 1483.36944 3358.10100 1733.18605 639.91846
## 608 609 610
## -5571.46746 8269.10752 -933.37608
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17308.65 20123.59 24329.40 24050.56 26377.28 23740.14 24448.34 19734.44
## 10 11 12 13 14 15 16 17
## 19473.99 16846.83 17615.50 14381.92 14432.51 15089.64 16767.00 15106.05
## 18 19 20 21 22 23 24 25
## 16129.00 15509.63 22511.88 21606.26 21092.01 22960.61 22294.23 22939.39
## 26 27 28 29 30 31 32 33
## 24764.45 18761.58 20468.19 28216.77 28273.78 27950.39 25606.65 26993.20
## 34 35 36 37 38 39 40 41
## 30792.73 31137.21 32530.43 30069.22 34084.14 37255.90 34345.34 31192.33
## 42 43 44 45 46 47 48 49
## 30053.15 20739.36 28172.95 30581.84 31658.71 38419.38 37919.69 42529.09
## 50 51 52 53 54 55 56 57
## 46717.38 39497.98 34127.94 29207.01 22429.06 28647.84 25267.20 21606.73
## 58 59 60 61 62 63 64 65
## 25965.17 27210.45 27504.10 27911.26 23828.95 40232.35 42055.43 37355.22
## 66 67 68 69 70 71 72 73
## 41514.52 46374.51 56921.87 54958.13 40368.44 37902.54 40886.27 35250.96
## 74 75 76 77 78 79 80 81
## 30754.70 21563.42 24728.64 20700.49 22762.93 17716.27 19708.88 18944.16
## 82 83 84 85 86 87 88 89
## 17982.90 16073.70 17337.00 20904.20 25271.89 26250.55 26275.22 26884.97
## 90 91 92 93 94 95 96 97
## 30963.34 29807.13 30795.17 28881.53 28090.41 28454.38 28856.36 22506.55
## 98 99 100 101 102 103 104 105
## 25487.44 18596.84 17465.93 15528.85 15825.21 16494.92 20916.90 20010.79
## 106 107 108 109 110 111 112 113
## 23482.12 23274.47 24931.60 27775.79 25302.24 21786.00 22058.30 24674.60
## 114 115 116 117 118 119 120 121
## 35416.04 33629.36 35446.02 38410.38 40344.13 38058.74 32938.42 29321.26
## 122 123 124 125 126 127 128 129
## 31380.49 29679.93 30848.10 38361.77 38008.28 37080.45 33979.51 35740.53
## 130 131 132 133 134 135 136 137
## 41077.26 40523.90 31825.28 33075.75 36229.37 32680.68 31086.23 30181.59
## 138 139 140 141 142 143 144 145
## 26777.63 28176.03 27948.27 25660.79 27662.27 26302.61 19977.23 22973.42
## 146 147 148 149 150 151 152 153
## 20824.74 23768.12 24301.99 25874.36 26054.37 27689.79 28975.79 31973.58
## 154 155 156 157 158 159 160 161
## 27495.09 26766.40 24349.37 30176.66 41446.35 39795.51 37190.32 42153.70
## 162 163 164 165 166 167 168 169
## 43546.30 46923.49 42440.81 37801.18 43164.93 59282.33 61469.39 59901.46
## 170 171 172 173 174 175 176 177
## 56763.14 55180.53 57852.47 56887.63 49267.12 52058.61 55758.81 55794.60
## 178 179 180 181 182 183 184 185
## 62842.62 53452.57 50240.35 41153.20 32788.45 36256.94 46245.88 45708.06
## 186 187 188 189 190 191 192 193
## 51593.34 57273.20 67933.94 73156.91 66923.08 67114.32 74104.48 69829.94
## 194 195 196 197 198 199 200 201
## 65403.52 54769.30 48867.11 50275.26 45919.91 38177.49 44529.40 42776.90
## 202 203 204 205 206 207 208 209
## 42418.97 42891.89 49608.88 58397.32 58031.13 59736.38 61373.40 65121.59
## 210 211 212 213 214 215 216 217
## 74480.79 66653.37 54976.33 49646.27 40744.69 37736.90 40815.26 30948.59
## 218 219 220 221 222 223 224 225
## 47729.66 54967.45 55859.12 78366.44 85776.46 87756.98 95262.25 86315.76
## 226 227 228 229 230 231 232 233
## 80382.24 79968.91 76656.87 75827.87 80511.17 81859.97 76358.53 71625.75
## 234 235 236 237 238 239 240 241
## 77215.29 64014.49 56141.30 48183.21 39889.90 44034.27 46162.60 39687.56
## 242 243 244 245 246 247 248 249
## 33438.79 43604.66 37916.79 41807.86 34159.46 32880.00 36493.83 39285.70
## 250 251 252 253 254 255 256 257
## 30219.16 36089.88 39848.20 44984.06 47671.55 47169.88 57354.01 74652.79
## 258 259 260 261 262 263 264 265
## 74469.94 67856.46 69321.49 65585.14 67014.88 60843.35 50239.61 46312.01
## 266 267 268 269 270 271 272 273
## 46519.05 42669.93 51374.22 47689.56 51789.27 49927.20 53948.44 54241.10
## 274 275 276 277 278 279 280 281
## 60190.30 57851.36 67414.63 61408.31 61615.96 59983.76 65661.51 59463.09
## 282 283 284 285 286 287 288 289
## 56065.91 45736.69 44151.61 61188.45 66655.84 67060.65 64507.53 63605.24
## 290 291 292 293 294 295 296 297
## 67560.84 71426.74 52638.75 42853.02 36931.96 47136.32 50341.40 49465.39
## 298 299 300 301 302 303 304 305
## 73387.39 79265.39 79925.48 84485.65 82704.64 77791.23 81218.76 56402.12
## 306 307 308 309 310 311 312 313
## 52705.62 52389.03 46249.58 43490.66 47053.16 39689.78 38426.35 33049.53
## 314 315 316 317 318 319 320 321
## 36791.18 35981.80 39769.85 37777.12 63271.25 61136.88 62742.06 70649.56
## 322 323 324 325 326 327 328 329
## 73009.84 98202.80 96598.84 72702.87 71514.34 69890.28 61933.18 59088.01
## 330 331 332 333 334 335 336 337
## 29376.84 33022.63 33457.18 35750.83 35099.13 40800.82 41864.25 37165.65
## 338 339 340 341 342 343 344 345
## 36388.96 36514.19 31886.17 37817.22 38473.12 38728.64 39594.53 41360.23
## 346 347 348 349 350 351 352 353
## 43161.60 42916.07 35940.59 26615.20 31899.37 30773.27 30367.96 28011.38
## 354 355 356 357 358 359 360 361
## 32637.31 36349.11 40762.21 38971.70 40217.72 42331.98 49643.55 50187.91
## 362 363 364 365 366 367 368 369
## 50387.29 52822.59 50334.49 49785.32 42513.72 39742.21 35962.43 33766.25
## 370 371 372 373 374 375 376 377
## 29868.64 37074.10 39335.18 47131.51 41171.34 40624.76 39178.61 38716.67
## 378 379 380 381 382 383 384 385
## 29687.64 34233.74 27364.00 35490.38 45706.58 49231.71 47524.60 49493.77
## 386 387 388 389 390 391 392 393
## 55643.58 65021.78 58309.84 52840.60 52572.37 59868.52 60385.98 68956.65
## 394 395 396 397 398 399 400 401
## 58185.48 59734.65 59299.73 58789.43 57293.43 56069.00 42977.27 51465.89
## 402 403 404 405 406 407 408 409
## 50477.61 49455.52 55784.48 48411.22 47730.16 46075.74 41801.82 40645.73
## 410 411 412 413 414 415 416 417
## 38731.48 32892.34 40676.21 43569.50 38301.99 33445.70 48149.41 51950.78
## 418 419 420 421 422 423 424 425
## 55835.68 48389.88 44754.70 43445.62 46991.60 35541.46 35273.72 29597.32
## 426 427 428 429 430 431 432 433
## 35124.06 43357.65 50171.99 46973.95 44075.73 41035.62 40925.07 37442.64
## 434 435 436 437 438 439 440 441
## 33624.61 30889.86 32448.17 34276.68 32303.81 37115.06 43250.56 40039.56
## 442 443 444 445 446 447 448 449
## 39748.88 42721.38 40631.30 44575.79 39876.33 31009.79 29860.37 41089.17
## 450 451 452 453 454 455 456 457
## 40774.30 46361.74 42047.10 42392.81 43995.04 47671.30 37659.79 42454.38
## 458 459 460 461 462 463 464 465
## 37929.25 45412.93 48893.27 51484.27 48250.70 50565.09 50764.35 52491.68
## 466 467 468 469 470 471 472 473
## 51994.95 54905.51 52263.92 57258.14 50594.08 48231.95 46834.04 43496.46
## 474 475 476 477 478 479 480 481
## 47210.23 54589.41 49079.21 50763.24 45610.84 44008.12 46809.12 36342.69
## 482 483 484 485 486 487 488 489
## 29976.84 31825.09 34491.36 35964.53 36919.50 30632.00 43020.94 49606.04
## 490 491 492 493 494 495 496 497
## 56359.31 51130.42 55908.97 63456.56 67225.00 53636.29 44320.39 42367.64
## 498 499 500 501 502 503 504 505
## 42687.20 43469.44 38018.33 40390.21 45631.94 51249.48 51951.15 52060.46
## 506 507 508 509 510 511 512 513
## 45833.42 47158.28 43459.69 46184.07 45853.53 39640.18 40758.50 39942.95
## 514 515 516 517 518 519 520 521
## 41036.11 43646.36 36567.25 31899.86 55520.07 63588.71 67200.44 60654.21
## 522 523 524 525 526 527 528 529
## 61978.58 75410.96 82308.96 57541.80 52469.72 49201.35 53530.92 53042.83
## 530 531 532 533 534 535 536 537
## 43337.17 48272.66 60790.30 55373.00 58732.30 62675.07 59760.93 54847.77
## 538 539 540 541 542 543 544 545
## 48383.83 47054.89 54902.43 54655.66 47299.68 49498.21 49326.83 50012.83
## 546 547 548 549 550 551 552 553
## 40767.39 32695.30 36988.10 45013.06 44812.32 46498.94 40576.39 49487.60
## 554 555 556 557 558 559 560 561
## 50629.86 40524.45 49957.68 57731.68 57103.79 60660.87 56475.91 68103.59
## 562 563 564 565 566 567 568 569
## 84603.72 74806.64 63499.38 67868.67 66014.26 67187.49 58852.72 43022.67
## 570 571 572 573 574 575 576 577
## 49952.13 55790.52 56959.93 59012.01 59651.74 56810.64 68918.90 58412.37
## 578 579 580 581 582 583 584 585
## 52206.30 59721.94 61208.93 54381.02 60576.73 56202.86 53280.83 66697.54
## 586 587 588 589 590 591 592 593
## 52290.82 59552.04 58654.69 52447.88 51760.90 52033.81 42854.63 45619.85
## 594 595 596 597 598 599 600 601
## 40307.05 44504.85 53169.04 46575.68 52368.42 54719.33 60324.29 56525.88
## 602 603 604 605 606 607 608 609
## 61285.18 52949.18 54807.92 55575.47 57857.53 58425.08 57971.04 52214.32
## 610
## 59196.09
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8349
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 7.240221 0.5528543 3.540297
## t2* 1767.045771 23.6324382 221.809740
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 2.801575 7.365043 14.23749
## 2 lag_depvar 1449.726721 1778.726325 2174.15546Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 14 01:04:11 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 14 01:04:22 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 14 01:04:33 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 14 01:04:44 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 14 01:04:55 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 14 01:05:05 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 14 01:05:16 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 14 01:05:27 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 14 01:05:38 2023
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 14 01:05:49 2023
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3)%>%
knitr::kable(format = "markdown", size=12, col.names= c("Item","2023","2022","2021","2020"))| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | 3.630286 | 5.410333 | 5.629750 | 6.5064884 |
| Comida | 351.597429 | 310.278417 | 314.087500 | 342.1973488 |
| Comunicaciones | 0.000000 | 0.000000 | 0.000000 | 0.0000000 |
| Electricidad | 39.311429 | 47.072333 | 38.297667 | 33.5250930 |
| Enceres | 26.902429 | 20.086417 | 17.443792 | 25.0026047 |
| Farmacia | 2.854286 | 1.831667 | 7.913875 | 8.7989302 |
| Gas/Bencina | 43.473714 | 44.325000 | 28.954333 | 28.0539535 |
| Diosi | 11.984286 | 31.180667 | 41.934250 | 35.7155349 |
| donaciones/regalos | 0.000000 | 0.000000 | 7.170083 | 6.3888140 |
| Electrodomésticos/ Mantención casa | 0.000000 | 3.944000 | 30.269500 | 19.2899535 |
| VTR | 12.567143 | 25.156667 | 22.121792 | 19.7269302 |
| Netflix | 5.771429 | 7.151583 | 7.090167 | 7.1983953 |
| Otros | 0.000000 | 3.151083 | 1.575542 | 0.8793721 |
| Total | 498.092429 | 499.588167 | 522.488250 | 533.2834186 |
## Joining with `by = join_by(word)`
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
## = T)`.
## Caused by warning:
## ! 41 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:2078, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2023-09-09 00:04:58 sería de: 36.613 pesos// Percentil 95% más alto proyectado: 39.771,07
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 36179.82 | 36177.46 |
| Lo.80 | 36191.63 | 36187.51 |
| Point.Forecast | 36612.72 | 37560.89 |
| Hi.80 | 38384.88 | 42320.21 |
| Hi.95 | 39357.46 | 44839.64 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.2450 1007.9316
## s.e. 0.1372 29.9385
##
## sigma^2 = 28818: log likelihood = -352.89
## AIC=711.78 AICc=712.26 BIC=717.75paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 intercept xreg
## 0.2217 714.6894 9.5292
## s.e. 0.1389 312.3797 10.0966
##
## sigma^2 = 28928: log likelihood = -352.46
## AIC=712.93 AICc=713.74 BIC=720.88forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) ## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 747.9152 | 664.7484 | 710.2107 |
| Lo.80 | 866.2449 | 783.5362 | 794.5381 |
| Point.Forecast | 1089.7748 | 1007.9316 | 982.1172 |
| Hi.80 | 1313.3048 | 1232.3271 | 1272.7341 |
| Hi.95 | 1431.6344 | 1351.1148 | 1459.9263 |
4. Gastos mensuales (resumen manual)
path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")
Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
#col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
skip=0)## Rows: 55 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.head(Gastos_casa_mensual_2022,5) %>%
knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")| n | mes_ano | Tami | Andrés |
|---|---|---|---|
| 1 | marzo_2019 | 175533 | 68268 |
| 2 | abril_2019 | 152640 | 55031 |
| 3 | mayo_2019 | 152985 | 192219 |
| 4 | junio_2019 | 291067 | 84961 |
| 5 | julio_2019 | 241389 | 205893 |
(
Gastos_casa_mensual_2022 %>%
reshape2::melt(id.var=c("n","mes_ano")) %>%
dplyr::mutate(gastador=as.factor(variable)) %>%
dplyr::select(-variable) %>%
ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
scale_color_manual(name="Gastador", values=c("red", "blue"))+
geom_line(size=1) +
#geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
ggtitle( "Gastos Mensuales (total manual)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
# scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
# scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
) %>% ggplotly()Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.3.0 bsts_0.9.9 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.11 scales_1.2.1 ggiraph_0.8.7
## [7] tidytext_0.4.1 DT_0.28 autoplotly_0.1.4
## [10] rvest_1.0.3 plotly_4.10.2 xts_0.13.1
## [13] forecast_8.21 wordcloud_2.6 RColorBrewer_1.1-3
## [16] SnowballC_0.7.1 tm_0.7-11 NLP_0.2-1
## [19] tsibble_1.1.3 lubridate_1.9.2 forcats_1.0.0
## [22] dplyr_1.1.2 purrr_1.0.1 tidyr_1.3.0
## [25] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
## [28] sjPlot_2.8.14 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.8.2 httr_1.4.6
## [34] readxl_1.4.3 zoo_1.8-12 stringr_1.5.0
## [37] stringi_1.7.12 data.table_1.14.8 reshape2_1.4.4
## [40] fUnitRoots_4021.80 plyr_1.8.8 readr_2.1.4
##
## loaded via a namespace (and not attached):
## [1] uuid_1.1-0 backports_1.4.1 systemfonts_1.0.4
## [4] selectr_0.4-2 lazyeval_0.2.2 splines_4.1.2
## [7] crosstalk_1.2.0 digest_0.6.31 htmltools_0.5.5
## [10] fansi_1.0.4 ggfortify_0.4.16 magrittr_2.0.3
## [13] tzdb_0.4.0 modelr_0.1.11 vroom_1.6.3
## [16] timechange_0.2.0 anytime_0.3.9 tseries_0.10-54
## [19] colorspace_2.1-0 xfun_0.39 crayon_1.5.2
## [22] jsonlite_1.8.4 lme4_1.1-34 glue_1.6.2
## [25] gtable_0.3.3 emmeans_1.8.7 sjstats_0.18.2
## [28] sjmisc_2.8.9 car_3.1-2 quantmod_0.4.24
## [31] abind_1.4-5 mvtnorm_1.2-2 DBI_1.1.3
## [34] ggeffects_1.2.3 Rcpp_1.0.10 viridisLite_0.4.2
## [37] xtable_1.8-4 performance_0.10.4 bit_4.0.5
## [40] htmlwidgets_1.6.2 timeSeries_4030.106 gplots_3.1.3
## [43] ellipsis_0.3.2 spatial_7.3-14 pkgconfig_2.0.3
## [46] farver_2.1.1 nnet_7.3-16 sass_0.4.5
## [49] dbplyr_2.3.3 janitor_2.2.0 utf8_1.2.3
## [52] tidyselect_1.2.0 labeling_0.4.2 rlang_1.1.0
## [55] munsell_0.5.0 cellranger_1.1.0 tools_4.1.2
## [58] cachem_1.0.7 cli_3.6.1 generics_0.1.3
## [61] sjlabelled_1.2.0 broom_1.0.5 evaluate_0.20
## [64] fastmap_1.1.1 yaml_2.3.7 knitr_1.43
## [67] bit64_4.0.5 caTools_1.18.2 nlme_3.1-153
## [70] slam_0.1-50 xml2_1.3.3 tokenizers_0.3.0
## [73] compiler_4.1.2 rstudioapi_0.14 curl_5.0.0
## [76] bslib_0.4.2 highr_0.10 fBasics_4022.94
## [79] Matrix_1.6-0 its.analysis_1.6.0 nloptr_2.0.3
## [82] urca_1.3-3 vctrs_0.6.1 pillar_1.9.0
## [85] lifecycle_1.0.3 lmtest_0.9-40 jquerylib_0.1.4
## [88] estimability_1.4.1 bitops_1.0-7 insight_0.19.3
## [91] R6_2.5.1 KernSmooth_2.23-20 janeaustenr_1.0.0
## [94] codetools_0.2-18 assertthat_0.2.1 boot_1.3-28
## [97] MASS_7.3-54 gtools_3.9.4 withr_2.5.0
## [100] fracdiff_1.5-2 bayestestR_0.13.1 parallel_4.1.2
## [103] hms_1.1.3 quadprog_1.5-8 timeDate_4022.108
## [106] minqa_1.2.5 snakecase_0.11.0 rmarkdown_2.23
## [109] carData_3.0-5 TTR_0.24.3 base64enc_0.1-3#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Packages')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({
'font-family': 'Helvetica Neue',
'font-size': '50%',
'code-inline-font-size': '15%',
'white-space': 'nowrap',
'line-height': '0.75em',
'min-height': '0.5em'
});",#;
"}")))